Simple and Sustainable Preparation of Nonactivated Porous Carbon from Brewing Waste for High-Performance Lithium–Sulfur Batteries

The development of renewable energy sources requires the parallel development of sustainable energy storage systems because of its noncontinuous production. Even the most-used battery on the planet, the lithium-ion battery, is reaching its technological limit. In light of this, lithium–sulfur batteries have emerged as one of the most promising technologies to address this problem. The use of biomass to produce cathodes for these batteries addresses not only the aforementioned problem, but it also reduces the carbon footprint and gives added value to something normally considered waste. Here, the production, by simple and nonactivating pyrolysis, of a carbon material using the abundant “after-boiling waste” derived from beer brewing is reported. After adding a high sulfur loading (70 %) to this biowaste-derived carbon by the “melt diffusion” method, the sulfur–carbon composite is used as an effective cathode in Li–S batteries. The cathode shows excellent performance, reaching high capacity values with long-term cyclability at high current—847 mAh g−1 at 1 C, 586 mAh g−1 at 2 C, and even 498 mAh g−1 at 5 C after 400 cycles—drastically reducing capacity loss to values approaching 0.01 % per cycle. This work demonstrates the possibility of obtaining low-cost, highly sustainable cathodic materials for the design of advanced energy storage systems

Phenols and Melanoidins as Natural Antioxidants in Beer. Structure, Reactivity and Antioxidant Activity

Producción CientíficaBeer is one of the most consumed drinks around the world, containing a variety of compounds that offer both appreciated sensorial characteristics and health advantages. Important healthy compounds in beer are those with antioxidant properties that attenuate the content of free radicals produced as by-products in the human metabolism, exerting an appreciable effect against cancers or cardiovascular diseases. This work details a study of antioxidant compounds present in beer, focusing on the two main groups: phenols (including polyphenolic forms) and melanoidins, formed specifically during brewing as Maillard products. The fundaments of the most important methods to evaluate beer antioxidant activity, the main antioxidant compounds present in beer—especially those with healthy properties—and the new trends to increase beer antioxidant activity are also discussed

Improving the performance of biomass-derived carbons in rechargable lithium batteries

III Encuentro sobre Nanociencia y Nanotecnología de Investigadores y Tecnólogos Andaluce

A motivational model based on artificial biological functions for the intelligent decision-making of social robots

Modelling the biology behind animal behaviour has attracted great interest in recent years. Nevertheless, neuroscience and artificial intelligence face the challenge of representing and emulating animal behaviour in robots. Consequently, this paper presents a biologically inspired motivational model to control the biological functions of autonomous robots that interact with and emulate human behaviour. The model is intended to produce fully autonomous, natural, and behaviour that can adapt to both familiar and unexpected situations in human–robot interactions. The primary contribution of this paper is to present novel methods for modelling the robot’s internal state to generate deliberative and reactive behaviour, how it perceives and evaluates the stimuli from the environment, and the role of emotional responses. Our architecture emulates essential animal biological functions such as neuroendocrine responses, circadian and ultradian rhythms, motivation, and affection, to generate biologically inspired behaviour in social robots. Neuroendocrinal substances control biological functions such as sleep, wakefulness, and emotion. Deficits in these processes regulate the robot’s motivational and affective states, significantly influencing the robot’s decision-making and, therefore, its behaviour. We evaluated the model by observing the long-term behaviour of the social robot Mini while interacting with people. The experiment assessed how the robot’s behaviour varied and evolved depending on its internal variables and external situations, adapting to different conditions. The outcomes show that an autonomous robot with appropriate decision-making can cope with its internal deficits and unexpected situations, controlling its sleep–wake cycle, social behaviour, affective states, and stress, when acting in human–robot interactions.The research leading to these results has received funding from the projects: Robots Sociales para Estimulación Física, Cognitiva y Afectiva de Mayores (ROSES), RTI2018-096338-B-I00, funded by the Ministerio de Ciencia, Innovación y Universidades; Robots sociales para mitigar la soledad y el aislamiento en mayores (SOROLI), PID2021-123941OA-I00, funded by Agencia Estatal de Investigación (AEI), Spanish Ministerio de Ciencia e Innovación. This publication is part of the R&D&I project PLEC2021-007819 funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR

Adaptive Circadian Rhythms for Autonomous and Biologically Inspired Robot Behavior

Biological rhythms are periodic internal variations of living organisms that act as adaptive responses to environmental changes. The human pacemaker is the suprachiasmatic nucleus, a brain region involved in biological functions like homeostasis or emotion. Biological rhythms are ultradian (less than 24 h), circadian (~24 h), or infradian (>24 h) depending on their period. Circadian rhythms are the most studied since they regulate daily sleep, emotion, and activity. Ambient and internal stimuli, such as light or activity, influence the timing and the period of biological rhythms, making our bodies adapt to dynamic situations. Nowadays, robots experience unceasing development, assisting us in many tasks. Due to the dynamic conditions of social environments and human-robot interaction, robots exhibiting adaptive behavior have more possibilities to engage users by emulating human social skills. This paper presents a biologically inspired model based on circadian biorhythms for autonomous and adaptive robot behavior. The model uses the Dynamic Circadian Integrated Response Characteristic method to mimic human biology and control artificial biologically inspired functions influencing the robot's decision-making. The robot's clock adapts to light, ambient noise, and user activity, synchronizing the robot's behavior to the ambient conditions. The results show the adaptive response of the model to time shifts and seasonal changes of different ambient stimuli while regulating simulated hormones that are key in sleep/activity timing, stress, and autonomic basal heartbeat control during the day

Modelling Multimodal Dialogues for Social Robots Using Communicative Acts

Social Robots need to communicate in a way that feels natural to humans if they are to effectively bond with the users and provide an engaging interaction. Inline with this natural, effective communication, robots need to perceive and manage multimodal information, both as input and output, and respond accordingly. Consequently, dialogue design is a key factor in creating an engaging multimodal interaction. These dialogues need to be flexible enough to adapt to unforeseen circumstances that arise during the conversation but should also be easy to create, so the development of new applications gets simpler. In this work, we present our approach to dialogue modelling based on basic atomic interaction units called Communicative Acts. They manage basic interactions considering who has the initiative (the robot or the user), and what is his/her intention. The two possible intentions are either ask for information or give information. In addition, because we focus on one-to-one interactions, the initiative can only be taken by the robot or the user. Communicative Acts can be parametrised and combined in a hierarchical manner to fulfil the needs of the robot’s applications, and they have been equipped with built-in functionalities that are in charge of low-level communication tasks. These tasks include communication error handling, turn-taking or user disengagement. This system has been integrated in Mini, a social robot that has been created to assist older adults with cognitive impairment. In a case of use, we demonstrate the operation of our system as well as its performance in real human–robot interactions.The research leading to these results has received funding from the projects Development of social robots to help seniors with cognitive impairment (ROBSEN), funded by the Ministerio de Economia y Competitividad; RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, funded by “Programas de Actividades I+D en la Comunidad de Madrid” and cofunded by Structural Funds of the EU; and Robots sociales para estimulación física, cognitiva y afectiva de mayores (ROSES) RTI2018-096338-B-I00 funded by Agencia Estatal de Investigación (AEI), Ministerio de Ciencia, Innovación y Universidade

Speeding-up Action Learning in a Social Robot with Dyna-Q+: A Bioinspired Probabilistic Model Approach

Robotic systems that are developed for social and dynamic environments require adaptive mechanisms to successfully operate. Consequently, learning from rewards has provided meaningful results in applications involving human-robot interaction. In those cases where the robot's state space and the number of actions is extensive, dimensionality becomes intractable and this drastically slows down the learning process. This effect is specially notorious in one-step temporal difference methods because just one update is performed per robot-environment interaction. In this paper, we prove how the action-based learning of a social robot can be improved by combining classical temporal difference reinforcement learning methods, such as Q-learning or Q( λ), with a probabilistic model of the environment. This architecture, which we have called Dyna, allows the robot to simultaneously act and plan using the experience obtained during real human-robot interactions. Principally, Dyna improves classical algorithms in terms of convergence speed and stability, which strengthens the learning process. Hence, in this work we have embedded a Dyna architecture in our social robot, Mini, to endow it with the ability to autonomously maintain an optimal internal state while living in a dynamic environment

DMN for Data Quality Measurement and Assessment

Data Quality assessment is aimed at evaluating the suitability of a dataset for an intended task. The extensive literature on data quality describes the various methodologies for assessing data quality by means of data profiling techniques of the whole datasets. Our investigations are aimed to provide solutions to the need of automatically assessing the level of quality of the records of a dataset, where data profiling tools do not provide an adequate level of information. As most of the times, it is easier to describe when a record has quality enough than calculating a qualitative indicator, we propose a semi-automatically business rule-guided data quality assessment methodology for every record. This involves first listing the business rules that describe the data (data requirements), then those describing how to produce measures (business rules for data quality measurements), and finally, those defining how to assess the level of data quality of a data set (business rules for data quality assessment). The main contribution of this paper is the adoption of the OMG standard DMN (Decision Model and Notation) to support the data quality requirement description and their automatic assessment by using the existing DMN engines.Ministerio de Ciencia y Tecnología RTI2018-094283-B-C33Ministerio de Ciencia y Tecnología RTI2018-094283-B-C31European Regional Development Fund SBPLY/17/180501/00029

Baterías Li-ion de alta energía combinando silicio nanométrico y espinelas de alto potencial

II Encuentro sobre nanociencia y nanotecnología de investigadores y tecnólogos de la Universidad de Córdoba. NANOUC

A Stable High-Capacity Lithium-Ion Battery Using a Biomass-Derived Sulfur-Carbon Cathode and Lithiated Silicon Anode

A full lithium-ion-sulfur cell with a remarkable cycle life was achieved by combining an environmentally sustainable biomass-derived sulfur-carbon cathode and a pre-lithiated silicon oxide anode. X-ray diffraction, Raman spectroscopy, energy dispersive spectroscopy, and thermogravimetry of the cathode evidenced the disordered nature of the carbon matrix in which sulfur was uniformly distributed with a weight content as high as 75 %, while scanning and transmission electron microscopy revealed the micrometric morphology of the composite. The sulfur-carbon electrode in the lithium half-cell exhibited a maximum capacity higher than 1200 mAh gS−1, reversible electrochemical process, limited electrode/electrolyte interphase resistance, and a rate capability up to C/2. The material showed a capacity decay of about 40 % with respect to the steady-state value over 100 cycles, likely due to the reaction with the lithium metal of dissolved polysulfides or impurities including P detected in the carbon precursor. Therefore, the replacement of the lithium metal with a less challenging anode was suggested, and the sulfur-carbon composite was subsequently investigated in the full lithium-ion-sulfur battery employing a Li-alloying silicon oxide anode. The full-cell revealed an initial capacity as high as 1200 mAh gS−1, a retention increased to more than 79 % for 100 galvanostatic cycles, and 56 % over 500 cycles. The data reported herein well indicated the reliability of energy storage devices with extended cycle life employing high-energy, green, and safe electrode materials